


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case			 =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
_snake_case			 =	'''    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'''
class       UpperCAmelCase_   (   unittest.TestCase       ):

   '''simple docstring'''




   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   Dict       =							tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir		,							"models/bert/"					)					)
     lowerCamelCase					:   List[Any]       =							self.transformer_dir
     shutil.copy(
         os.path.join(__A		,							"src/transformers/models/bert/modeling_bert.py"					)		,							os.path.join(self.transformer_dir		,							"models/bert/modeling_bert.py"					)		,							)






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   Dict       =							"src/transformers"
     shutil.rmtree(self.transformer_dir					)






   def _snake_case       (						self		,							__A		,							__A		,							__A		,							__A=None					):






     """simple docstring"""





     lowerCamelCase					:   Any       =							comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
     if overwrite_result is not None:
       lowerCamelCase					:   Union[str, Any]       =							comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
     lowerCamelCase					:   List[Any]       =							black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119					)
     lowerCamelCase					:   Any       =							black.format_str(__A		,							mode=__A					)
     lowerCamelCase					:   Dict       =							os.path.join(self.transformer_dir		,							"new_code.py"					)
     with open(__A		,							"w"		,							newline="\n"					) as f:
       f.write(__A					)
     if overwrite_result is None:
       self.assertTrue(len(check_copies.is_copy_consistent(__A					)					) == 0					)
     else:
       check_copies.is_copy_consistent(f.name		,							overwrite=__A					)
       with open(__A		,							"r"					) as f:
         self.assertTrue(f.read()		,							__A					)






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   List[str]       =							check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"					)
     self.assertEqual(__A		,							__A					)






   def _snake_case       (						self					):






     """simple docstring"""





     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,							"BertLMPredictionHead"		,							REFERENCE_CODE + "\n"		,							)

     # With no empty line at the end
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,							"BertLMPredictionHead"		,							__A		,							)

     # Copy consistency with rename
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,							"TestModelLMPredictionHead"		,							re.sub("Bert"		,							"TestModel"		,							__A					)		,							)

     # Copy consistency with a really long name
     lowerCamelCase					:   Dict       =							"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
     self.check_copy_consistency(
         F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""		,							F"""{long_class_name}LMPredictionHead"""		,							re.sub("Bert"		,							__A		,							__A					)		,							)

     # Copy consistency with overwrite
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,							"TestModelLMPredictionHead"		,							__A		,							overwrite_result=re.sub("Bert"		,							"TestModel"		,							__A					)		,							)






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   Optional[Any]       =							check_copies.LOCALIZED_READMES["README_zh-hans.md"]

     lowerCamelCase					:   Optional[Any]       =							(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
         " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
         " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
         " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
         " Luong, Quoc V. Le, Christopher D. Manning."
     )
     lowerCamelCase					:   Optional[int]       =							(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     lowerCamelCase					:   List[Any]       =							(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
         " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
         " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
         " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
         " Christopher D. Manning 发布。\n"
     )

     lowerCamelCase       ,      lowerCamelCase					:   Tuple       =							check_copies.convert_to_localized_md(
         __A		,							__A		,							localized_readme["format_model_list"]					)

     self.assertFalse(__A					)
     self.assertEqual(__A		,							__A					)

     lowerCamelCase       ,      lowerCamelCase					:   Any       =							check_copies.convert_to_localized_md(
         __A		,							__A		,							localized_readme["format_model_list"]					)

     # Check whether the number of models is equal to README.md after conversion.
     self.assertTrue(__A					)

     lowerCamelCase					:   str       =							(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
     )
     lowerCamelCase					:   Dict       =							(
         "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
         " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     lowerCamelCase					:   str       =							(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )

     lowerCamelCase       ,      lowerCamelCase					:   Optional[Any]       =							check_copies.convert_to_localized_md(
         __A		,							__A		,							localized_readme["format_model_list"]					)

     # Check if the model link is synchronized.
     self.assertEqual(__A		,							__A					)




'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="van"






     def __init__(					self						:						Union[str, Any]    ,							a						:						List[Any]=2_24    ,							a						:						Optional[int]=3    ,							a						:						Optional[Any]=[7, 3, 3, 3]    ,							a						:						int=[4, 2, 2, 2]    ,							a						:						Optional[int]=[64, 1_28, 3_20, 5_12]    ,							a						:						str=[3, 3, 12, 3]    ,							a						:						str=[8, 8, 4, 4]    ,							a						:						int="gelu"    ,							a						:						int=0.02    ,							a						:						Optional[int]=1e-6    ,							a						:						List[str]=1e-2    ,							a						:						Union[str, Any]=0.0    ,							a						:						int=0.0    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_sizes
         __lowerCamelCase            =    strides
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    mlp_ratios
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    layer_norm_eps
         __lowerCamelCase            =    layer_scale_init_value
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    dropout_rate
